Taipei Tech AIoT Lab

Artificial Intelligence of Things

Lab Director

Prof. Kuan-Ting (K. T.) Lai 賴冠廷

Dr. Kuan-Ting Lai received bachelor's degree in Electric Engineering and master's degree in Computer Science from National Taiwan University in 2003 and 2005.
After graduation, he joined Quanta Computer as a video ASIC engineer for 4 years, and started to pursue Ph.D. in 2009, under supervision of Prof. Ming-Syan Chen.
During 2012-2013, Dr. Lai visited Prof. Shih-Fu Chang’s DVMM lab at Columbia University, and co-developed a large-scale video event detection system with IBM T. J. Watson Research Center.
He received his Ph.D. degree in Feb. 2015 and became the VP of technology at Arkados Group. He also co-founded AnyCharge, a wireless charging service provider in Asia.
In 2018, Dr. Lai joined the Department of Electronic Engineering at National Taipei University of Technology (臺北科技大學電子工程系) as an Assistant Professor.
His research interests include computer vision, machine learning, deep learning and Internet of Things. His habit is traveling around the world by joining conferences and making new friends.

News

Projects

VIVID is a photo-realistic simulator that aims to facilitate deep learning for computer vision, which supports different characters: robot, drone, and automobile.
The platform can be used for many research fields including deep reinforcement learning, semantic segmentation object recognition and human action recognition.
For more details please visit the project's github page.

AnyCharge is a public wireless charging service based on IoT technology. All the chargers are controllable by our cloud. We have deployed around 70 charging spots in Taiwan, Thailand, and Singapore (website).

In this project, we develop a large-scale action detector of drone that can recognize numerous complex human actions such as running, eating, smoking, photo shooting,
and create a large-scale dataset to evaluate our algorithm.

Telecom fraud is one of the most prevalent crimes today and causes most property loss. To identify the roles of the fraudsters, we cooperate with Criminal Investigation Bureau of Taiwan and propose a Telecom Fraud Analysis Model (TFAM).
For more details, please refer to our paper "Mining the Networks of Telecommunication Fraud Groups using Social Network Analysis", ASONAM, 2017.